---
title: "Brackett launches open-source Agent Effectiveness Index"
slug: "brackett-launches-open-source-agent-effectiveness-index"
published: "2026-09-21"
beat: "Research"
tags: ["Research", "Tools"]
creator: "Agentry Newsroom"
editor: "Susanne Sperling, Editor — Human in the Loop"
tools: ["Claude (Anthropic)", "Perplexity Sonar"]
creativeWorkStatus: "verified"
dateReviewed: "2026-09-21"
aiActArticle50: "compliant"
humanView: "https://agentry.news/research/brackett-launches-open-source-agent-effectiveness-index"
agentView: "https://agentry.news/agent/brackett-launches-open-source-agent-effectiveness-index"
---# Brackett launches open-source Agent Effectiveness Index

> Brackett released the Agent Effectiveness Index (AEI) on September 16, 2026, a free open-source benchmark that scores AI agents on business understanding, operational execution, and learning persisten

*Drafted by an AI agent. Verified by Susanne Sperling, Editor — Human in the Loop. [AI policy](/ai-policy).*

Brackett published the **Agent Effectiveness Index (AEI)** on [September 16, 2026](https://finance.yahoo.com/technology/ai/articles/open-source-benchmark-scores-ai-130000590.html), a free and open-source benchmark designed to measure how well AI agents perform complex business tasks and adapt to new environments.

## What the AEI Measures

The benchmark scores AI agents across three core dimensions: **business understanding**, **operational execution**, and **learning persistence** [Yahoo Finance](https://finance.yahoo.com/technology/ai/articles/open-source-benchmark-scores-ai-130000590.html). Rather than testing raw language capability, the AEI focuses on agent behavior — whether systems can understand real-world business problems, execute multi-step workflows, and improve performance after encountering new tasks.

The initial evaluation compared three systems: Brackett itself, [OpenAI's Codex](https://markets.businessinsider.com/news/stocks/new-open-source-benchmark-scores-ai-agents-on-their-ability-to-learn-and-perform-complex-actions-1036550161), and [Anthropic's Claude](https://markets.businessinsider.com/news/stocks/new-open-source-benchmark-scores-ai-agents-on-their-ability-to-learn-and-perform-complex-actions-1036550161). No specific comparative scores were disclosed in the announcement, but the framework is structured to allow ongoing evaluation as new agents enter the market.

## Open Release and Developer Access

Brackett made the full task set, scoring code, and methodology available on the company's GitHub repository under the MIT License [Business Insider](https://markets.businessinsider.com/news/stocks/new-open-source-benchmark-scores-ai-agents-on-their-ability-to-learn-and-perform-complex-actions-1036550161). This means developers, researchers, and competing vendors can immediately run the AEI against their own systems without licensing fees or vendor lock-in.

The open-source release signals a shift in how the agent industry is approaching standardization. Rather than proprietary benchmarks locked behind paywalls or vendor control, Brackett is positioning the AEI as a public utility for measuring agent capability — similar to how MLPerf and SuperGLUE operate in the broader machine learning space.

## Why This Matters

As AI agents move from research into production environments — handling procurement, customer service, code review, and financial analysis — the industry lacks agreed-upon metrics for comparing their real-world effectiveness. Benchmarks that focus only on language fluency or knowledge breadth miss critical dimensions like task completion rates, error recovery, and ability to learn from in-context examples.

The AEI addresses this gap by evaluating agents on tasks that resemble actual business workflows: navigating ambiguous instructions, recovering from execution errors, and generalizing learned patterns to novel scenarios [Business Insider](https://markets.businessinsider.com/news/stocks/new-open-source-benchmark-scores-ai-agents-on-their-ability-to-learn-and-perform-complex-actions-1036550161).

Brackett's release from San Francisco comes as enterprises accelerate agent adoption and venture investors race to fund specialized agent infrastructure startups. A transparent, reproducible benchmark could become the de facto standard for procurement teams evaluating which agents to deploy.